At a Glance
- Tasks: Enhance execution algorithms and conduct quantitative research in a dynamic trading environment.
- Company: Join Goldman Sachs, a leading global investment banking firm with a commitment to innovation.
- Benefits: Enjoy competitive salary, health insurance, generous vacation, and professional development opportunities.
- Other info: Collaborative culture with excellent career growth and mentorship opportunities.
- Why this job: Make a real impact on financial markets while working with cutting-edge technology and data.
- Qualifications: Advanced degree in a quantitative field and experience in trading algorithms required.
The predicted salary is between 80000 - 100000 £ per year.
Goldman Sachs Electronic Trading (GSET) sits at the intersection of technology, quantitative research, and global markets. We design and operate the firm's suite of electronic execution algorithms that enable institutional clients to access liquidity and execute orders efficiently. Within GSET, the Algo R&D team is responsible for the research, design, and continuous improvement of our execution algorithm platform. We combine deep expertise in market microstructure, statistical modelling, and machine learning with world-class engineering to build algorithms that optimise execution quality, minimise market impact, and adapt intelligently to real-time market conditions. Our work spans the full lifecycle of algorithmic trading - from research into price formation and liquidity dynamics, through model development and back testing, to production deployment and live performance monitoring. We partner closely with traders, technologists, sales teams, and clients to ensure our algorithms remain at the forefront of the industry.
As a member of the London based Algo R&D team, you will join a collaborative, intellectually rigorous group that values innovation, scientific integrity, and real world impact. You will have access to one of the most comprehensive datasets in the industry, cutting edge infrastructure, and a global network of experts - all in service of solving some of the most challenging problems in modern financial markets.
Who We Look For
- Individuals who combine intellectual curiosity with commercial pragmatism.
- First principles thinkers who understand the assumptions behind models and know when to challenge or adapt them.
- Collaborative partners who thrive in a team environment and enjoy working across disciplines.
- Impact oriented individuals who measure success by the impact on execution quality.
- Continuous learners who stay at the frontier of quantitative research.
- Culture carriers who contribute to an inclusive, high performance team culture.
Responsibilities
- Enhance execution algorithms for cash equities.
- Conduct rigorous quantitative research on market microstructure, order book dynamics, venue analysis, and transaction cost analysis (TCA).
- Build and maintain statistical and machine learning models for short term price prediction, fill rate estimation, market impact modelling, and optimal order placement/scheduling.
- Collaborate with technology teams to productionize research into low latency, high reliability trading systems.
- Perform back testing, simulation, and live A/B testing of algorithm enhancements; define and track performance metrics.
- Analyse large scale tick data to identify alpha opportunities and areas for algo improvement.
- Partner with sales, trading, and client facing teams to translate client feedback and business requirements into research priorities.
- Stay current with academic literature, regulatory changes, and competitive landscape in electronic trading.
- Present research findings and strategic recommendations to senior stakeholders and cross functional partners.
Basic Qualifications
- Advanced degree (Master's or PhD) in a quantitative discipline.
- 5+ years of experience in quantitative research related to execution/trading algorithms.
- Deep understanding of market microstructure concepts.
- Proven experience with statistical modelling, time series analysis, and/or machine learning applied to financial data.
- Proficiency in working with large datasets.
- Solid grasp of transaction cost analysis (TCA) methodologies and execution benchmarks.
- Excellent communication skills.
Preferred Qualifications
- Experience with equities execution algos in European or global markets.
- Understanding of regulatory frameworks relevant to algorithmic trading.
- Strong programming skills in Python.
- Ability to query data in kdb+/q.
- Familiarity with reinforcement learning or deep learning techniques applied to optimal execution problems.
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